LLM4AAMAS: initial version

README.md

 # LLM4AAMAS
 
-Les Systèmes Multi-agents et Agents Autonomes (SMAA) génératifs ouvrent des
-perspectives prometteuses pour résoudre des problèmes dans des environnements
-ouverts et simuler des dynamiques sociales complexes.
+Generative Autonomous Agents and Multi-Agent Systems (AAMAS) offer promising
+opportunities for solving problems in open environments and simulating complex
+social dynamics.
 
+This repository contains a collection of papers and ressources related
+to generative AAMAS. This list is a work in progress and will be regularly updated with new resources.
 
-L'objectif de ce dépôt est de constituer une collection de ressources
-pertinentes pour les SMAA génératifs que nous nous efforçons de mettre à jour
-régulièrement et en continu. 
 
-## Auteurs
+## Artificial Intelligence
+
+- **[Intelligence artificielle : une approche moderne (4e édition)](https://hal.archives-ouvertes.fr/hal-04245057)**
+   *Stuart Russell, Peter Norvig, Fabrice Popineau, Laurent Miclet, Claire Cadet (2021)*
+   Publisher: Pearson France
+
+- **[Apprentissage artificiel - 3e édition : Deep learning, concepts et algorithmes](https://www.eyrolles.com/)**
+   *Antoine Cornuéjols, Laurent Miclet, Vincent Barra (2018)*
+   Publisher: Eyrolles
+
+
+## Neural networks (RNN, Transformers)
+
+- **[Learning representations by back-propagating errors](https://doi.org/10.1038/323533a0)**
+   *David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams (1986)*
+   Published in *Nature*
+
+-  **[ImageNet Classification with Deep Convolutional Neural Networks](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)**
+   *Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton (2012)*
+   Presented at *NeurIPS*
+
+
+## Large Language Models
+
+- **[A Survey of Large Language Models](https://arxiv.org/abs/2303.18223)**
+    *Wayne Xin Zhao, Kun Zhou, Junyi Li, et al. (2024)*
+    Published on *arXiv*
+
+- **[Large Language Model based Multi-Agents: A Survey of Progress and
+  Challenges](https://arxiv.org/abs/2402.01680)** *Taicheng Guo et al. (2024)*
+  Published on *arXiv* arXiv:2402.01680 [cs.CL]
+
+- **[Improving language understanding by generative
+    pre-training](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)**
+    *Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever (2018)*
+    Published by OpenAI
+
+- **[BERT: Pre-training of Deep Bidirectional Transformers for Language
+   Understanding](https://www.aclweb.org/anthology/N19-1423/)** *Jacob Devlin,
+   Ming-Wei Chang, Kenton Lee, Kristina Toutanova (2019)* Presented at
+   *NAACL-HLT*
+
+- **[Sequence to Sequence Learning with Neural
+   Networks](https://arxiv.org/abs/1409.3215)** *Ilya Sutskever, Oriol Vinyals,
+   Quoc V. Le (2014)* Published on *arXiv*
+
+- **[Learning Phrase Representations using RNN Encoder-Decoder for Statistical
+    Machine Translation](https://arxiv.org/abs/1406.1078)** *Kyunghyun Cho, Bart
+    van Merrienboer, Caglar Gulcehre, et al. (2014)* Published on *arXiv*
+
+## Tuning
+
+### Instruction tuning
+
+- **[LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685)**
+    *Edward J. Hu, Yelong Shen, Phillip Wallis, et al. (2021)*
+    Published on *arXiv*
+
+- **[Language Models are Few-Shot
+   Learners](https://papers.nips.cc/paper/2020/file/fc2c7f9a3f3f86cde5d8ad2c7f7e57b2-Paper.pdf)**
+   *Tom Brown, Benjamin Mann, Nick Ryder, et al. (2020)* Presented at *NeurIPS*
+
+### Alignement tuning
+
+- **[Training language models to follow instructions with human
+   feedback](https://papers.nips.cc/paper/2022/hash/17f4c5f98073d1fb95f7e53f5c7fdb64-Abstract.html)**
+   *Long Ouyang, Jeffrey Wu, Xu Jiang, et al. (2022)* Presented at *NeurIPS*
+
+## Existing LLMs
+
+Many models are available at the following URLs:  
+[https://www.nomic.ai/gpt4all](https://www.nomic.ai/gpt4all) and  
+[https://huggingface.co/models](https://huggingface.co/models).
+
+- **[GPT-4 Technical Report](https://arxiv.org/abs/2303.08774)**
+    *OpenAI Team (2024)*
+    Published on *arXiv*
+
+- **[The Llama 3 Herd of Models](https://arxiv.org/abs/2407.21783)**
+    *Meta Team (2024)*
+    Published on *arXiv*
+
+- **[Stanford Alpaca: An Instruction-Following LLaMa Model](https://github.com/tatsu-lab/stanford_alpaca)**
+    *Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, et al. (2023)*
+    Published on *GitHub*
+
+- **[Mixtral of Experts](https://arxiv.org/abs/2401.04088)**  
+    *Mistral AI team (2024)*  
+    Published on *arXiv*
+
+- **[Mistral 7B](https://arxiv.org/abs/2310.06825)**  
+    *Mistral AI team (2023)*  
+    Published on *arXiv*
+
+- **[The Lucie-7B LLM and the Lucie Training Dataset: Open Resources for
+  Multilingual Language Generation](https://arxiv.org/abs/)** *Olivier Gouvert,
+  Julie Hunter, Jérôme Louradour, Evan Dufraisse, Yaya Sy, Pierre-Carl Langlais,
+  Anastasia Stasenko, Laura Rivière, Christophe Cerisara, Jean-Pierre Lorré
+  (2025)*
+
+
+## Prompt engineering
+
+### ICL
+
+- **A Survey on In-context Learning** *Qingxiu Dong, Lei Li, Damai Dai, Ce
+  Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Baobao Chang,
+  Xu Sun, Lei Li, Zhifang Sui (2024)* Presented at the *Conference on Empirical
+  Methods in Natural Language Processing (EMNLP)* Location: Miami, Florida, USA
+  Published by: Association for Computational Linguistics
+
+### CoT
+
+- **[Chain-of-Thought Prompting Elicits Reasoning in Large Language
+  Models](https://papers.nips.cc/paper/52604-chain-of-thought-prompting-elicits-reasoning-in-large-language-models)**
+  *Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, et al. (2022)*
+  Presented at *NeurIPS*
+
+
+### RAG
+
+- **[Retrieval-Augmented Generation for Large Language Models: A
+  Survey](https://arxiv.org/abs/2312.10997)** *Yunfan Gao, Yun Xiong, Xinyu Gao,
+  Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Meng Wang, Haofen Wang
+  (2024)* Published on *arXiv*
+
+## Generative Autonomous Agents
+
+- **A Survey on Large Language Model Based Autonomous Agents** Lei Wang, Chen
+  Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai
+  Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Jirong Wen (2024)*
+  Published in *Frontiers of Computer Science* (Volume 18, Issue 6, Pages
+  186345) Publisher: Springer
+
+
+- **[HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging
+  Face](https://papers.nips.cc/paper/2023/hash/38154-hugginggpt-solving-ai-tasks-with-chatgpt-and-its-friends-in-hugging-face.pdf)**
+  *Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang
+  (2023)* Presented at *Advances in Neural Information Processing Systems
+  (NeurIPS)* Pages: 38154–38180 Publisher: Curran Associates, Inc. Volume: 36
+
+- **[Toolformer: Language Models Can Teach Themselves to Use Tools](https://papers.nips.cc/paper/86759-toolformer-language-models-can-teach-themselves-to-use-tools)**
+   *Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, et al. (2023)*
+   Presented at *NeurIPS*
+
+- **[Cognitive Architectures for Language Agents](https://arxiv.org/abs/2309.02427)**  
+    *Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths (2024)*  
+    Published on *arXiv*
+
+
+### Generative Autonomous Agents on the shelf
+
+- [LangChain](https://www.langchain.com) is an open-source framework for
+  designing prompts for LLMs. It can be used to define high-level reasoning
+  sequences, conversational agents, RAGs (Retrieval-Augmented Generation),
+  document summaries, or even the generation of synthetic data.
+
+- [LangGraph](https://langchain-ai.github.io/langgraph) is a low-level library
+  for the design of cognitive architecture for autonomous agents, whose
+  reasoning engine is an LLM.
+
+- [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) is a platform for
+  the creation, deployment, and management of generative agents.
+
+- [WorkGPT](https://github.com/team-openpm/workgpt) is similar to AutoGPT
+
+
+## Generative MAS
+
+- **[Large language models empowered agent-based modeling and simulation: A
+  survey and perspectives](https://doi.org/10.1057/s41599-024-01235-9)** **Chen
+  Gao, Xiaochong Lan, Nian Li, Yuan Yuan, Jingtao Ding, Zhilun Zhou, Fengli Xu,
+  Yong Li (2024)* Published in *Humanities and Social Sciences Communications*,
+  Volume 11, Issue 1, Pages 1–24
+
+- **[Social Simulacra: Creating Populated Prototypes for Social Computing
+  Systems](https://dl.acm.org/doi/10.1145/3526110.3545617)** *Joon Sung Park,
+  Lindsay Popowski, Carrie Cai, Meredith Ringel Morris, Percy Liang, Michael S.
+  Bernstein (2022)* Published in *Proceedings of the 35th Annual ACM Symposium
+  on User Interface Software and Technology* Articleno: 74, Pages: 18, Location:
+  Bend, OR, USA
+
+- **[Generative Agents: Interactive Simulacra of Human
+  Behavior](https://dl.acm.org/doi/10.1145/3586184.3594067)** *Joon Sung Park,
+  Joseph O'Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, Michael
+  S. Bernstein (2023)* Published in *Proceedings of the 36th Annual ACM
+  Symposium on User Interface Software and Technology* Articleno: 2, Pages: 22,
+  Location: San Francisco, CA, USA, Series: UIST '23
+
+- **[Agentverse: Facilitating multi-agent collaboration and exploring emergent
+  behaviors](https://openreview.net/forum?id=HywBMyh6JGR)** *Weize Chen, Yusheng
+  Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chi-Min Chan, Heyang Yu, Yaxi Lu,
+  Yi-Hsin Hung, Chen Qian, et al. (2023)* Published in *The Twelfth
+  International Conference on Learning Representations (ICLR 2023)*
+
+- **[Training socially aligned language models on simulated social
+  interactions](https://arxiv.org/abs/2305.16960)** *Ruibo Liu, Ruixin Yang,
+  Chenyan Jia, Ge Zhang, Denny Zhou, Andrew M. Dai, Diyi Yang, Soroush Vosoughi
+  (2023)* Published on *arXiv* arXiv:2305.16960
+
+- [S3: Social-network Simulation System with Large Language Model-Empowered
+  Agents](https://arxiv.org/abs/2307.14984)** *Chen Gao, Xiaochong Lan, Zhihong
+  Lu, Jinzhu Mao, Jinghua Piao, Huandong Wang, Depeng Jin, Yong Li (2023)*
+  Published on *arXiv* arXiv:2307.14984
+
+### Generative MAS on the shelf
+
+- [MetaGPT](https://github.com/geekan/MetaGPT) is a framework for creating
+  generative MAS dedicated to software development.
+
+- [CAMEL](https://github.com/camel-ai/camel) proposes a generative multi-agent
+  framework for accomplishing complex tasks.
+
+- [AutoGen](https://github.com/microsoft/autogen) is a versatile open-source
+  framework for creating generative multi-agent systems.
+  
+## Authors
 
 Maxime MORGE
 
 ## License
 
-TBA
+This program is free software: you can redistribute it and/or modify it under
+the terms of the GNU General Public License as published by the Free Software
+Foundation, either version 3 of the License, or (at your option) any later
+version.
+
+This program is distributed in the hope that it will be useful, but WITHOUT ANY
+WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
+PARTICULAR PURPOSE. See the GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License along with
+this program. If not, see <http://www.gnu.org/licenses/>.